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International Journal of Science, Strategic Management and Technology

An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases · DOI via Crossref The journal adheres to established scholarly publishing, peer-review, and research ethics guidelines set by the UGC

ISSN: 3108-1762 (Online)
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COMPUTER-AIDED DRUG DESIGN AND SYNTHESIS OF POTENTIAL ANTICANCER MOLECULES TARGETING EGFR

AUTHORS:
Amit S. Choudhary
Mentor
Prof. Kunal A. Joshi
Affiliation

Department of Clinical Pharmacy,
Horizon Institute of Pharmacy & Technology, India

CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

Epidermal Growth Factor Receptor (EGFR) is a transmembrane receptor tyrosine kinase that plays a pivotal role in regulating cellular proliferation, differentiation, survival, and migration. Aberrant activation or overexpression of EGFR has been strongly implicated in the pathogenesis and progression of various human cancers, including non-small cell lung cancer (NSCLC), breast cancer, colorectal cancer, and head and neck squamous cell carcinoma. Consequently, EGFR has emerged as a prominent molecular target for anticancer drug discovery. In recent years, computer-aided drug design (CADD) has revolutionized the process of anticancer drug development by enabling the rational design, screening, and optimization of lead compounds with enhanced efficacy and reduced toxicity. This research article presents a comprehensive overview of the application of CADD approaches in the design and synthesis of potential anticancer molecules targeting EGFR. The study integrates structure-based and ligand-based computational techniques, including molecular docking, pharmacophore modeling, quantitative structure–activity relationship (QSAR) analysis, and molecular dynamics (MD) simulations, to identify promising EGFR inhibitors. Selected lead compounds were subjected to in silico ADMET profiling to evaluate their drug-likeness and safety profiles. Furthermore, the synthetic strategies employed for the preparation of selected molecules are discussed, along with their predicted biological activity. The results highlight the effectiveness of CADD tools in accelerating EGFR-targeted drug discovery and provide valuable insights for the development of next-generation anticancer therapeutics.

Keywords
Computer-aided drug design; EGFR inhibitors; anticancer agents; molecular docking; QSAR; molecular dynamics; targeted therapy
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Choudhary, A. S. (2026). Computer-Aided Drug Design and Synthesis of Potential Anticancer Molecules Targeting EGFR. International Journal of Science, Strategic Management and Technology, 02(02), 1-9. https://doi.org/10.55041/ijsmt.v2i2.003

Choudhary, Amit. "Computer-Aided Drug Design and Synthesis of Potential Anticancer Molecules Targeting EGFR." International Journal of Science, Strategic Management and Technology, vol. 02, no. 02, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v2i2.003.

Choudhary, Amit. "Computer-Aided Drug Design and Synthesis of Potential Anticancer Molecules Targeting EGFR." International Journal of Science, Strategic Management and Technology 02, no. 02 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v2i2.003.

References

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2.               Yarden Y, Sliwkowski MX. Untangling the ErbB signalling network. Nat Rev Mol Cell Biol. 2001.


3.               Cohen MH, et al. FDA drug approval summary: gefitinib. Oncologist. 2003.


4.               Trott O, Olson AJ. AutoDock Vina: improving docking speed and accuracy. J Comput Chem. 2010.


5.               Lipinski CA. Lead- and drug-like compounds. Drug Discov Today. 2004.


6.               Vucicevic, J., Nikolic, K., & Mitchell, J. B. O. (2019). Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches. Current Medicinal Chemistry, 26(21), 3874–3889. https://doi.org/10.2174/0929867324666170712115411


7.               Nada, H., Gul, A. R., Elkamhawy, A., Kim, S., Kim, M., Choi, Y., Park, T. J., & Lee, K. (2023). Machine Learning-Based Approach to Developing Potent EGFR Inhibitors for Breast Cancer-Design, Synthesis, and In Vitro Evaluation. ACS Omega, 8(35), 31784–31800. https://doi.org/10.1021/acsomega.3c02799


8.               Regales, L., Gong, Y., Shen, R., De Stanchina, E., Vivanco, I., Goel, A., Koutcher, J. A., Spassova, M., Ouerfelli, O., Mellinghoff, I. K., Zakowski, M. F., Politi, K. A., & Pao, W. (2009). Dual targeting of EGFR can overcome a major drug resistance mutation in mouse models of EGFR mutant lung cancer. Journal of Clinical Investigation, 119(10). https://doi.org/10.1172/jci38746


9.               Gaber, A. A., El-Morsy, A. M., Sherbiny, F. F., Bayoumi, A. H., El-Gamal, K. M., El-Adl, K., Al-Karmalawy, A. A., Ezz Eldin, R. R., Saleh, M. A., & Abulkhair, H. S. (2021). Pharmacophore-linked pyrazolo[3,4-d]pyrimidines as EGFR-TK inhibitors: Synthesis, anticancer evaluation, pharmacokinetics, and in silico mechanistic studies. Archiv Der Pharmazie, 358(3). https://doi.org/10.1002/ardp.202100258


10.            Garg, P., Singhal, G., Kulkarni, P., Horne, D., Salgia, R., & Singhal, S. S. (2024). Artificial Intelligence-Driven Computational Approaches in the Development of Anticancer Drugs. Cancers, 16(22), 3884. https://doi.org/10.3390/cancers16223884

Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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